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lint(docs): list inpaint mode, clarify headers

This commit is contained in:
Sean Sube 2023-01-09 23:27:27 -06:00
parent 778cf6e7d1
commit de86df9ea9
1 changed files with 20 additions and 12 deletions

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@ -31,9 +31,12 @@ This is still an early project and the instructions are a little rough, but it w
- can be hosted alongside API or on a CDN
- built with React and MUI
- txt2img mode
- outputs are saved to file
- show image history
- image controls and scheduler selection
- with recent image history
- img2img mode
- image upload with preview
- guided by prompt and negative prompt
- inpainting mode
- mask painting
- source to mask conversion tools
@ -47,10 +50,10 @@ This is still an early project and the instructions are a little rough, but it w
- [Install Git and Python](#install-git-and-python)
- [Create a virtual environment](#create-a-virtual-environment)
- [Install pip packages](#install-pip-packages)
- [For AMD on Windows: Install ORT nightly package](#for-amd-on-windows-install-ort-nightly-package)
- [For CPU on Linux: Install CPU PyTorch](#for-cpu-on-linux-install-cpu-pytorch)
- [For CPU on Windows: Install CPU PyTorch](#for-cpu-on-windows-install-cpu-pytorch)
- [For Nvidia everywhere: Install GPU PyTorch and ONNX](#for-nvidia-everywhere-install-gpu-pytorch-and-onnx)
- [For AMD on Windows: Install ONNX DirectML](#for-amd-on-windows-install-onnx-directml)
- [For CPU on Linux: Install PyTorch CPU](#for-cpu-on-linux-install-pytorch-cpu)
- [For CPU on Windows: Install PyTorch CPU](#for-cpu-on-windows-install-pytorch-cpu)
- [For Nvidia everywhere: Install PyTorch GPU and ONNX GPU](#for-nvidia-everywhere-install-pytorch-gpu-and-onnx-gpu)
- [Download and convert models](#download-and-convert-models)
- [Usage](#usage)
- [Configuring and running the server](#configuring-and-running-the-server)
@ -180,7 +183,7 @@ sure you are not using `numpy>=1.24`.
[This SO question](https://stackoverflow.com/questions/74844262/how-to-solve-error-numpy-has-no-attribute-float-in-python)
has more details.
#### For AMD on Windows: Install ORT nightly package
#### For AMD on Windows: Install ONNX DirectML
If you are running on Windows, install the DirectML ONNX runtime as well:
@ -202,7 +205,7 @@ download the `cp39` package, and so on. Installing with pip will figure out the
Make sure to include the `--force-reinstall` flag, since it requires some older versions of other packages, and will
overwrite the versions you currently have installed.
#### For CPU on Linux: Install CPU PyTorch
#### For CPU on Linux: Install PyTorch CPU
If you are running with a CPU and no hardware acceleration, install `onnxruntime` and the CPU version of PyTorch:
@ -210,7 +213,7 @@ If you are running with a CPU and no hardware acceleration, install `onnxruntime
> pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
```
#### For CPU on Windows: Install CPU PyTorch
#### For CPU on Windows: Install PyTorch CPU
If you are running with a CPU and no hardware acceleration, install `onnxruntime` and the CPU version of PyTorch:
@ -218,7 +221,7 @@ If you are running with a CPU and no hardware acceleration, install `onnxruntime
> pip install torch
```
#### For Nvidia everywhere: Install GPU PyTorch and ONNX
#### For Nvidia everywhere: Install PyTorch GPU and ONNX GPU
If you are running with an Nvidia GPU, install `onnxruntime-gpu`:
@ -398,10 +401,15 @@ custom config using:
- make sure the API and GUI are both running
- make sure you are using the correct hostname or IP address
- open the appropriate firewall ports:
- TCP/5000 for the API
- TCP/3000 or TCP/8000 for the GUI (3000 is the dev server)
- TCP/5000 for the API dev server
- TCP/3000 for the GUI dev server
- TCP/80 for the GUI using nginx without a container
- TCP/8000 for the GUI using the nginx container
- CUDA errors:
- make sure you are using CUDA 11.x
- https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements
- numpy invalid combination of arguments:
- make sure to export ONNX models using the same packages and versions that you use while running the API
- numpy `np.float` missing
- reinstall `pip install "numpy>=1.20,<1.24 --force-reinstall"`
- another package may have upgraded numpy to 1.24, which removed that symbol